


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: List[Any]				   =					'scheduler_config.json'






class _lowerCamelCase(		_snake_case  ):
       lowercase_					:      Union[str, Any]       					=     1
       lowercase_					:      Any       					=     2
       lowercase_					:      Dict       					=     3
       lowercase_					:      List[Any]       					=     4
       lowercase_					:      int       					=     5






@dataclass
class _lowerCamelCase(		_snake_case  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Optional[Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      Optional[Any]       					=     ["""dtype"""]
       lowercase_					:      Optional[int]       					=     []
       lowercase_					:      Union[str, Any]       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=__snake_case, subfolder=__snake_case, return_unused_kwargs=__snake_case, **__snake_case, )
           _lowercase			:				Optional[int] 						=						cls.from_config(__snake_case, return_unused_kwargs=__snake_case, **__snake_case)

           if hasattr(__snake_case, 'create_state') and getattr(__snake_case, 'has_state', __snake_case):
               _lowercase			:				List[str] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=__snake_case, push_to_hub=__snake_case, **__snake_case)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Optional[int] 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				str 						=						[
               getattr(__snake_case, __snake_case) for c in compatible_classes_str if hasattr(__snake_case, __snake_case)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    assert len(SCREAMING_SNAKE_CASE__					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE__					) - x.ndim)					)				,					SCREAMING_SNAKE_CASE__					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		Optional[Any]:





    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				Any 						=						[]
    for i in range(SCREAMING_SNAKE_CASE__					):
        _lowercase			:				Optional[Any] 						=						i / num_diffusion_timesteps
        _lowercase			:				Tuple 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE__					) / alpha_bar(SCREAMING_SNAKE_CASE__					)				,					SCREAMING_SNAKE_CASE__					)					)
    return jnp.array(SCREAMING_SNAKE_CASE__				,					dtype=SCREAMING_SNAKE_CASE__					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				Optional[int] 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				Union[str, Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[Any] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				List[Any] 						=						jnp.cumprod(__snake_case, axis=0)

           return cls(
               alphas=__snake_case, betas=__snake_case, alphas_cumprod=__snake_case, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    _lowercase			:				int 						=						state.alphas_cumprod

    _lowercase			:				List[Any] 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Dict 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				List[str] 						=						broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE__				,					original_samples.shape					)

    _lowercase			:				Tuple 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				List[str] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				Optional[Any] 						=						broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE__				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    _lowercase			:				Any 						=						get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__					)
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Any:
    _lowercase			:				Optional[Any] 						=						get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__				,					SCREAMING_SNAKE_CASE__					)
    _lowercase			:				Optional[int] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity






'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  List[Any]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Any				  =						{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Dict       =    """gpt_bigcode"""
							A_				: str       =    ["""past_key_values"""]
							A_				: Tuple       =    {
							    """hidden_size""": """n_embd""",
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(       self       ,   __snake_case=5_0257       ,   __snake_case=1024       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=None       ,   __snake_case="gelu_pytorch_tanh"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=1e-5       ,   __snake_case=0.02       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=5_0256       ,   __snake_case=5_0256       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=True       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= n_positions
											_SCREAMING_SNAKE_CASE					:			List[Any]							= n_embd
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_layer
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_head
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= n_inner
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= activation_function
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= resid_pdrop
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= embd_pdrop
											_SCREAMING_SNAKE_CASE					:			List[Any]							= attn_pdrop
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= layer_norm_epsilon
											_SCREAMING_SNAKE_CASE					:			Any							= initializer_range
											_SCREAMING_SNAKE_CASE					:			str							= scale_attn_weights
											_SCREAMING_SNAKE_CASE					:			Tuple							= use_cache
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			List[Any]							= scale_attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			Any							= multi_query

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= bos_token_id
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= eos_token_id

											super().__init__(bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   **__snake_case    )




